# Boosting multiple sclerosis lesion segmentation through attention mechanism

arXiv: https://arxiv.org/abs/2304.10790 · HTML: https://mlsanity.com/paper/2304.10790/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2023.

## Built on by (most cited first, 8 of 8)

- [Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers](https://mlsanity.com/paper/2404.09326/) (2024)
- [ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation -- Methods and Results](https://mlsanity.com/paper/2410.07924/) (2024)
- [unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features](https://mlsanity.com/paper/2308.15507/) (2023)
- [UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2508.03982/) (2025)
- [Toward Generalizable Multiple Sclerosis Lesion Segmentation Models](https://mlsanity.com/paper/2410.19623/) (2024)
- [Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration](https://mlsanity.com/paper/2310.09221/) (2023)
- [Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models](https://mlsanity.com/paper/2605.09666/) (2026)
- [Attention-Based Segmentation of WMHs and Differentiation of Vascular vs. Demyelinating Lesions](https://mlsanity.com/paper/2607.08171/) (2026)

## Abstract (arXiv)

Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial intelligence, fully automated analysis is not yet available. State-of-the-art methods rely on slight variations in segmentation architectures (e.g. U-Net, etc.). However, recent research has demonstrated how exploiting temporal-aware features and attention mechanisms can provide a significant boost to traditional architectures. This paper proposes a framework that exploits an augmented U-Net architecture with a convolutional long short-term memory layer and attention mechanism which is able to segment and quantify multiple sclerosis lesions detected in magnetic resonance images. Quantitative and qualitative evaluation on challenging examples demonstrated how the method outperforms previous state-of-the-art approaches, reporting an overall Dice score of 89% and also demonstrating robustness and generalization ability on never seen new test samples of a new dedicated under construction dataset.